Attribute-Based Encryption (ABE) has been caught a wide range of attention owing to its advantage of achieving both fine-grained access control and protection for data. In recent years, the constructions of ABE have developed to lattice-based schemes, while most of them exist bottlenecks in efficiency and security as a result of constructing on the Learning With Errors (LWE) assumption and relying on a single central authority. In this paper, we propose a decentralized multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme from the Ring Learning With Errors (R-LWE) assumption, which supports flexible access policies and is built on a new trapdoor with efficient algorithms. Moreover, in our system, management and distribution for the private keys are accomplished by multiple authorities utilizing the technique of Shamir threshold secret sharing, which makes it more efficient and applicable to the distributed storing environments. We then prove that the scheme is selective secure under chosen plaintext attacks. The experimental results manifest that the proposed scheme can be carried out in a comparatively short amount of time, which confirms its practicality.

The advent of Industry 4.0 has made people pay more and more attention to the security of the industrial control system. As a common and typical communication protocol, Modbus does not consider the problem of data security at the beginning of its design, which provides opportunities for criminals. In this paper, we design a security scheme to protect the traditional Modbus TCP protocol, by using domestic encryption algorithms. As a result, the proposed scheme is able to identity authentication, data encryption, data integrity check, and anti-replay attacks. Security analysis and experimental results show that our proposed scheme solves the security problem of Modbus TCP protocol with minimal overhead increase.

In recent years, with the rapid development of mobile Internet and 5G technology, great changes have been brought to our lives, and human beings have stepped into the era of big data. These new features and techniques in 5G support many different types of mobile applications for users, which makes network security extremely challenging. Among them, more and more applications involve users’ private data, such as location information, financial information, and biological information. In order to prevent users’ privacy disclosure, most applications choose to use private protocols. However, such private protocols also provide a means for malware and malicious applications to steal users’ privacy and confidential data. From a more secure point of view, we need to provide a way for users to know how many private protocols are running on their mobile phones and distinguish which are authorized applications and which are not. Therefore, the analysis and identification of private protocols have become a hot topic in current research. How to extract the characteristics of network protocol effectively and identify the private protocol accurately becomes the most important part of this research. In this paper, we combine genetic algorithm and association rule algorithm and then propose a set of feature extraction algorithm and protocol recognition algorithm for unknown protocols. The experimental analysis based on the actual data shows that these methods can effectively solve the problems of feature extraction and recognition for unknown protocols and can greatly improve the accuracy of private protocol recognition.

Deep hashing models have been widely adopted to tackle the challenges of large-scale image retrieval. However, these approaches face serious security risks due to their vulnerability to adversarial examples. Despite the increasing exploration of targeted attacks on deep hashing models, existing approaches still suffer from a lack of multimodal guidance, reliance on labeling information and dependence on pixel-level operations for attacks. To address these limitations, we proposed DiffHash, a novel diffusion-based targeted attack for deep hashing. Unlike traditional pixel-based attacks that directly modify specific pixels and lack multimodal guidance, our approach focuses on optimizing the latent representations of images, guided by text information generated by a Large Language Model (LLM) for the target image. Furthermore, we designed a multispace hash alignment network to align the high-dimensional image space and text space to the low-dimensional binary hash space. During reconstruction, we also incorporated text-guided attention mechanisms to refine adversarial examples, ensuring them aligned with the target semantics while maintaining visual plausibility. Extensive experiments have demonstrated that our method outperforms state-of-the-art (SOTA) targeted attack methods, achieving better black-box transferability and offering more excellent stability across datasets. The code is available at https://github.com/Raineasy/DiffHash.